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This paper demonstrates how different scientific disciplines can be successfully merged for efficient real life applications. We propose a novel approach to a social science application using artificial intelligence, by suggesting a neural network designto anticipate or predict people’s perceptions regarding the Cyprus conflict and the peace mediation process.Our novel model is based on associating people’s views and thoughts on critical issues related to the conflict together with their anticipation for securing a peace treaty by year 2020. Our database is our own and has been collected through completed surveys by the international relations department students at the Near East University. The obtained digitally-coded values of the entire database as well as the proposed associative neural predicting model can be further used in training and testing the neural model, which certainly waives the way for further research in this rare application area for artificial intelligence. [ABSTRACT FROM AUTHOR]

Our thirst for progress as humans is reflected by our continuous research activities in different areas leading to many useful emerging applications and technologies. Artificial intelligence and its applications are good examples of such explored fields with varying expectations and realistic results. Generally, artificially intelligent systems have shown their capability in solving real-life problems; particularly in non-linear tasks. Such tasks are often assigned to an artificial neural network (ANN) model to arbitrate as they mimic the structure and function of a biological brain; albeit at a basic level. In this paper, we investigate a newly emerging application area for ANNs; namely civil engineering. We design, implement and test an ANN model to predict and classify the compressive strength of different concrete mixes into low, moderate or high strength. Traditionally, the performance of concrete is affected by many non-linear factors and testing its strength comprises a destructive procedure of concrete samples. Numerical results in this work show high efficiency in correctly classifying the compressive strength, thus making it possible to use in real-life applications. [ABSTRACT FROM AUTHOR]

The preservation of our natural environment and ecological system has recently become an alarming global concern due to the increase in worldwide pollution levels. Many agencies, governmental and non-governmental organizations are leading awareness campaigns of the dwindling ecosystem in various environments. One of these environments is marine life which forms an important integral part of realizing a self-sustaining ecosystem. Unfortunately, some marine dwellers such as Chelonioidea turtles are becoming endangered species. This is also partly due to lack of information on the more rare and mysterious Chelonioidea species; their habitats, traveling routes and tracks. There are eight species of these mysterious marine dwellers in the world. This paper presents a novel application that could aid in preserving the international marine life ecological system. Our intelligent identification system is based on pattern recognition and neural learning of visual information in sea turtles images. The system is scale and rotational invariant and thus can be successfully used in recognizing the species via their images. [ABSTRACT FROM AUTHOR]

The relationship between environmental pollution and economic growth has lately been the focus of discussion among policy makers and scientists worldwide. In Turkey, the agriculture sector is a major contributor to the national economy and consequently growth in agriculture leads to economic growth; which in turn affects pollution levels. In this paper, we present a novel application of neural networks based on using the simple and yet efficient back propagation learning algorithm. We estimate the carbon dioxide (CO 2 ) emission levels by approximating the non-linear relationship between agricultural factors and alterations in CO 2 emission levels. We use a public Turkish dataset spanning 1968-2010 and showing recorded major agricultural and economical indictors and the level of CO 2 . The experimental results indicate that such approximation is successfully possible and encouraging to be used in more similar non-linear applications. [ABSTRACT FROM AUTHOR]

The idea of machines having emotions sounds like science fiction, however, few decades ago the idea of machines with intelligence seemed also like fiction, but today we are developing intelligent machines with successful applications. We have always overlooked the emotional factors during machine learning and decision making; however, it is quite conceivable to artificially model certain emotions in machine learning. This paper presents an emotional neural network (EmNN) that is based on the emotional back propagation (EmBP) learning algorithm. The EmNN has emotional weights and two emotional parameters; anxiety and confidence, which are updated during learning. The performances of the EmNN and a conventional BP-based neural network, using two topologies for each network, will be compared when applied to a blood cell type identification problem. Experimental results show that the additional emotional parameters and weights improved the identification rate as well as the classification time. [ABSTRACT FROM AUTHOR]